There are large differences so to get a better sense, here is the same comparison but with area log transformed.
Data split into species groups and by IUCN category ***
This plot shows the total percent of a species range map that is (1) only covered by IUCN cells, (2) only covered by AquaMaps cells and (3) covered by both IUCN and AquaMaps cells.
Here is an example of a single species, the yellow speckled chromis (Chromis alpha)
For Chromis alpha, 3.14% of the total area is covered by both IUCN and AquaMaps, 3.18% is covered just by AquaMaps and 77.3% is covered just by IUCN.
This plot shows that the majority of species range maps have only ~30% of their full map covered by both maps, and very few species have more than 50% of their two ranges (IUCN and AquaMaps) overlapping.
Split up by species - not sure if this is useful or not
For each range
Exploring possible correlation between total species range area and percent overlap of smaller range within the larger:
spp_list1 <- spp_list %>%
group_by(spp_group) %>%
mutate(n_spp = n()) %>%
ungroup()
spp_gp_q1 <- spp_list1 %>%
filter(area_ratio >= median(area_ratio, na.rm = TRUE) & sm_perc >= median(sm_perc, na.rm = TRUE)) %>%
group_by(spp_group) %>%
summarize(n_spp = first(n_spp),
n_spp_q1 = n())
spp_gp_q2 <- spp_list1 %>%
filter(area_ratio < median(area_ratio, na.rm = TRUE) & sm_perc >= median(sm_perc, na.rm = TRUE)) %>%
group_by(spp_group) %>%
summarize(n_spp = first(n_spp),
n_spp_q2 = n())
spp_gp_q4 <- spp_list1 %>%
filter(area_ratio < median(area_ratio, na.rm = TRUE) & sm_perc < median(sm_perc, na.rm = TRUE)) %>%
group_by(spp_group) %>%
summarize(n_spp = first(n_spp),
n_spp_q4 = n())
spp_gp_q3 <- spp_list1 %>%
filter(area_ratio >= median(area_ratio, na.rm = TRUE) & sm_perc < median(sm_perc, na.rm = TRUE)) %>%
group_by(spp_group) %>%
summarize(n_spp = first(n_spp),
n_spp_q3 = n())
spp_gp_quadrants <- spp_gp_q1 %>%
left_join(spp_gp_q2, by = c('spp_group', 'n_spp')) %>%
left_join(spp_gp_q3, by = c('spp_group', 'n_spp')) %>%
left_join(spp_gp_q4, by = c('spp_group', 'n_spp')) %>%
gather(quad, n_quad, n_spp_q1, n_spp_q2, n_spp_q3, n_spp_q4) %>%
mutate(quad = str_replace(quad, 'n_spp_', ''),
pct_quad = n_quad/n_spp)
spp_gp_quadrants <- spp_gp_quadrants %>%
left_join(spp_gp_quadrants %>%
filter(quad == 'q1') %>%
select(spp_group, pct_q1 = pct_quad),
by = 'spp_group')
barchart_spp_gp_quads <- ggplot(spp_gp_quadrants %>%
transform(spp_group = reorder(spp_group, pct_q1)),
aes(x = spp_group, fill = quad, weight = pct_quad)) +
ggtheme_plot +
geom_bar(stat = 'bin') +
geom_text(aes(label = sprintf('n = %s', n_spp), y = .05), hjust = 0) +
coord_flip() +
labs(x = 'Species Group',
y = 'Relative number of species per quadrant',
title = 'Species group breakdown by quadrant')
##
## Call:
## lm(formula = total_area ~ sm_perc, data = spp_list)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31528842 -12533425 -1793686 5117681 341380989
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10860595 2496562 -4.35 1.43e-05 ***
## sm_perc 425500 36394 11.69 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 33120000 on 1885 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.06761, Adjusted R-squared: 0.06712
## F-statistic: 136.7 on 1 and 1885 DF, p-value: < 2.2e-16
Percent coverage as function of area, clipping to area below 100,000,000 km^2:
Percent coverage as a function of log(area)
This looks at the full Aquamaps dataset… should it be limited to species with valid categories?
Comparing three scenarios to the current (v2015) global model for SPP:
Boxplots: